Overview

Dataset statistics

Number of variables23
Number of observations108014
Missing cells650
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.0 MiB
Average record size in memory184.0 B

Variable types

Numeric10
Text7
Categorical3
DateTime3

Alerts

DEALKEY is highly overall correlated with DEALDETKEYHigh correlation
DEALDETKEY is highly overall correlated with DEALKEYHigh correlation
DEALVALUE is highly overall correlated with ORDER_PRICE and 2 other fieldsHigh correlation
ORDER_PRICE is highly overall correlated with DEALVALUE and 2 other fieldsHigh correlation
ORIGINAL_PRICE is highly overall correlated with DEALVALUE and 2 other fieldsHigh correlation
ORIGINAL_PRICE_NO_DISCOUNT_PERS is highly overall correlated with DEALVALUE and 2 other fieldsHigh correlation
CHANNELID is highly overall correlated with CHANNELDSCHigh correlation
CHANNELDSC is highly overall correlated with CHANNELIDHigh correlation
DEALVALUE is highly skewed (γ1 = 328.585931)Skewed
DEALQTY is highly skewed (γ1 = 149.6666236)Skewed
ORDER_PRICE is highly skewed (γ1 = 116.3891148)Skewed
ORIGINAL_PRICE is highly skewed (γ1 = 116.1697965)Skewed
ORIGINAL_PRICE_NO_DISCOUNT_PERS is highly skewed (γ1 = 119.9378342)Skewed
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
DEALDETKEY has unique valuesUnique
CUSTOMER_DISCOUNT_PERS has 84031 (77.8%) zerosZeros
DISCOUNT_PCT has 48159 (44.6%) zerosZeros

Reproduction

Analysis started2023-09-25 13:08:36.046710
Analysis finished2023-09-25 13:08:49.205455
Duration13.16 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct108014
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54006.5
Minimum0
Maximum108013
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size844.0 KiB
2023-09-25T15:08:49.297237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5400.65
Q127003.25
median54006.5
Q381009.75
95-th percentile102612.35
Maximum108013
Range108013
Interquartile range (IQR)54006.5

Descriptive statistics

Standard deviation31181.1
Coefficient of variation (CV)0.57735829
Kurtosis-1.2
Mean54006.5
Median Absolute Deviation (MAD)27003.5
Skewness0
Sum5.8334581 × 109
Variance9.7226102 × 108
MonotonicityStrictly increasing
2023-09-25T15:08:49.388919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
71992 1
 
< 0.1%
72016 1
 
< 0.1%
72015 1
 
< 0.1%
72014 1
 
< 0.1%
72013 1
 
< 0.1%
72012 1
 
< 0.1%
72011 1
 
< 0.1%
72010 1
 
< 0.1%
72009 1
 
< 0.1%
Other values (108004) 108004
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
108013 1
< 0.1%
108012 1
< 0.1%
108011 1
< 0.1%
108010 1
< 0.1%
108009 1
< 0.1%
108008 1
< 0.1%
108007 1
< 0.1%
108006 1
< 0.1%
108005 1
< 0.1%
108004 1
< 0.1%

DEALKEY
Real number (ℝ)

HIGH CORRELATION 

Distinct46464
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3691601.1
Minimum3639796
Maximum3743622
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size844.0 KiB
2023-09-25T15:08:49.492028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3639796
5-th percentile3644567
Q13665658.2
median3691903
Q33717626
95-th percentile3737135
Maximum3743622
Range103826
Interquartile range (IQR)51967.75

Descriptive statistics

Standard deviation29850.113
Coefficient of variation (CV)0.0080859531
Kurtosis-1.210069
Mean3691601.1
Median Absolute Deviation (MAD)25996
Skewness-0.031073182
Sum3.987446 × 1011
Variance8.9102927 × 108
MonotonicityNot monotonic
2023-09-25T15:08:49.588473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3734160 191
 
0.2%
3735316 164
 
0.2%
3716278 162
 
0.1%
3708521 160
 
0.1%
3696477 149
 
0.1%
3741258 148
 
0.1%
3708646 143
 
0.1%
3702275 129
 
0.1%
3708519 123
 
0.1%
3710995 115
 
0.1%
Other values (46454) 106530
98.6%
ValueCountFrequency (%)
3639796 1
< 0.1%
3639798 2
< 0.1%
3639799 1
< 0.1%
3639800 2
< 0.1%
3639801 1
< 0.1%
3639802 1
< 0.1%
3639803 1
< 0.1%
3639806 2
< 0.1%
3639807 2
< 0.1%
3639808 1
< 0.1%
ValueCountFrequency (%)
3743622 1
 
< 0.1%
3743621 1
 
< 0.1%
3743618 1
 
< 0.1%
3743615 1
 
< 0.1%
3743613 1
 
< 0.1%
3743611 3
< 0.1%
3743610 1
 
< 0.1%
3743607 1
 
< 0.1%
3743598 1
 
< 0.1%
3743597 1
 
< 0.1%

DEALDETKEY
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct108014
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12111106
Minimum11986442
Maximum12238490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size844.0 KiB
2023-09-25T15:08:49.674097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11986442
5-th percentile11998538
Q112049075
median12111432
Q312173418
95-th percentile12221243
Maximum12238490
Range252048
Interquartile range (IQR)124343.25

Descriptive statistics

Standard deviation71712.392
Coefficient of variation (CV)0.005921209
Kurtosis-1.197274
Mean12111106
Median Absolute Deviation (MAD)62173.5
Skewness-0.016600549
Sum1.3081691 × 1012
Variance5.1426672 × 109
MonotonicityNot monotonic
2023-09-25T15:08:49.763268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12089692 1
 
< 0.1%
12189573 1
 
< 0.1%
12078727 1
 
< 0.1%
11993357 1
 
< 0.1%
12138390 1
 
< 0.1%
12029379 1
 
< 0.1%
12004464 1
 
< 0.1%
12032124 1
 
< 0.1%
12221414 1
 
< 0.1%
12219510 1
 
< 0.1%
Other values (108004) 108004
> 99.9%
ValueCountFrequency (%)
11986442 1
< 0.1%
11986445 1
< 0.1%
11986446 1
< 0.1%
11986448 1
< 0.1%
11986449 1
< 0.1%
11986450 1
< 0.1%
11986452 1
< 0.1%
11986456 1
< 0.1%
11986457 1
< 0.1%
11986460 1
< 0.1%
ValueCountFrequency (%)
12238490 1
< 0.1%
12238489 1
< 0.1%
12238481 1
< 0.1%
12238475 1
< 0.1%
12238472 1
< 0.1%
12238470 1
< 0.1%
12238469 1
< 0.1%
12238468 1
< 0.1%
12238467 1
< 0.1%
12238404 1
< 0.1%
Distinct5666
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size844.0 KiB
2023-09-25T15:08:49.890966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1080140
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1158 ?
Unique (%)1.1%

Sample

1st row053e113f9b
2nd rowb6fc01a771
3rd row9b2a287a25
4th row64354f1917
5th rowdf63f09ef0
ValueCountFrequency (%)
216e149e5b 1315
 
1.2%
1972a513cf 863
 
0.8%
3142781c56 566
 
0.5%
2ab0d23c98 542
 
0.5%
2eab4308e0 538
 
0.5%
b8320bfadc 532
 
0.5%
48c887035b 527
 
0.5%
7ee47b2b4b 521
 
0.5%
ea9ce80d4b 492
 
0.5%
19323567eb 462
 
0.4%
Other values (5656) 101656
94.1%
2023-09-25T15:08:50.097406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 71414
 
6.6%
3 70942
 
6.6%
2 70617
 
6.5%
b 70035
 
6.5%
4 69914
 
6.5%
1 69456
 
6.4%
c 67961
 
6.3%
f 67931
 
6.3%
a 67377
 
6.2%
5 66632
 
6.2%
Other values (6) 387861
35.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 671196
62.1%
Lowercase Letter 408944
37.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 70942
10.6%
2 70617
10.5%
4 69914
10.4%
1 69456
10.3%
5 66632
9.9%
7 66066
9.8%
9 65844
9.8%
8 65794
9.8%
0 64348
9.6%
6 61583
9.2%
Lowercase Letter
ValueCountFrequency (%)
e 71414
17.5%
b 70035
17.1%
c 67961
16.6%
f 67931
16.6%
a 67377
16.5%
d 64226
15.7%

Most occurring scripts

ValueCountFrequency (%)
Common 671196
62.1%
Latin 408944
37.9%

Most frequent character per script

Common
ValueCountFrequency (%)
3 70942
10.6%
2 70617
10.5%
4 69914
10.4%
1 69456
10.3%
5 66632
9.9%
7 66066
9.8%
9 65844
9.8%
8 65794
9.8%
0 64348
9.6%
6 61583
9.2%
Latin
ValueCountFrequency (%)
e 71414
17.5%
b 70035
17.1%
c 67961
16.6%
f 67931
16.6%
a 67377
16.5%
d 64226
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1080140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 71414
 
6.6%
3 70942
 
6.6%
2 70617
 
6.5%
b 70035
 
6.5%
4 69914
 
6.5%
1 69456
 
6.4%
c 67961
 
6.3%
f 67931
 
6.3%
a 67377
 
6.2%
5 66632
 
6.2%
Other values (6) 387861
35.9%

CHANNELID
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size844.0 KiB
CS
37780 
SM
32704 
VN
8644 
CC
8392 
VS
6959 
Other values (20)
13535 

Length

Max length6
Median length2
Mean length2.024071
Min length2

Characters and Unicode

Total characters218628
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowSM
2nd rowCS
3rd rowSM
4th rowCS
5th rowSM

Common Values

ValueCountFrequency (%)
CS 37780
35.0%
SM 32704
30.3%
VN 8644
 
8.0%
CC 8392
 
7.8%
VS 6959
 
6.4%
TE 3320
 
3.1%
FU 2018
 
1.9%
AN 1782
 
1.6%
VE 1534
 
1.4%
VO 1063
 
1.0%
Other values (15) 3818
 
3.5%

Length

2023-09-25T15:08:50.197263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cs 37780
35.0%
sm 32704
30.3%
vn 8644
 
8.0%
cc 8392
 
7.8%
vs 6959
 
6.4%
te 3320
 
3.1%
fu 2018
 
1.9%
an 1782
 
1.6%
ve 1534
 
1.4%
vo 1063
 
1.0%
Other values (15) 3818
 
3.5%

Most occurring characters

ValueCountFrequency (%)
S 78833
36.1%
C 54832
25.1%
M 32705
15.0%
V 18326
 
8.4%
N 10428
 
4.8%
E 4854
 
2.2%
T 3442
 
1.6%
A 3028
 
1.4%
F 2019
 
0.9%
U 2018
 
0.9%
Other values (11) 8143
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 214728
98.2%
Lowercase Letter 2600
 
1.2%
Open Punctuation 650
 
0.3%
Close Punctuation 650
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 78833
36.7%
C 54832
25.5%
M 32705
15.2%
V 18326
 
8.5%
N 10428
 
4.9%
E 4854
 
2.3%
T 3442
 
1.6%
A 3028
 
1.4%
F 2019
 
0.9%
U 2018
 
0.9%
Other values (6) 4243
 
2.0%
Lowercase Letter
ValueCountFrequency (%)
l 1300
50.0%
n 650
25.0%
u 650
25.0%
Open Punctuation
ValueCountFrequency (%)
( 650
100.0%
Close Punctuation
ValueCountFrequency (%)
) 650
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 217328
99.4%
Common 1300
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 78833
36.3%
C 54832
25.2%
M 32705
15.0%
V 18326
 
8.4%
N 10428
 
4.8%
E 4854
 
2.2%
T 3442
 
1.6%
A 3028
 
1.4%
F 2019
 
0.9%
U 2018
 
0.9%
Other values (9) 6843
 
3.1%
Common
ValueCountFrequency (%)
( 650
50.0%
) 650
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 218628
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 78833
36.1%
C 54832
25.1%
M 32705
15.0%
V 18326
 
8.4%
N 10428
 
4.8%
E 4854
 
2.2%
T 3442
 
1.6%
A 3028
 
1.4%
F 2019
 
0.9%
U 2018
 
0.9%
Other values (11) 8143
 
3.7%

CHANNELDSC
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)< 0.1%
Missing650
Missing (%)0.6%
Memory size844.0 KiB
OFFICE PRODUCTS SALES AREA
37780 
SALES UNIT MEDIUM BUSINESS
32704 
CORPORATE NORD
8644 
CONTACT CENTER
8392 
CORPORATE CENTRO SUD
6959 
Other values (19)
12885 

Length

Max length30
Median length26
Mean length23.258215
Min length8

Characters and Unicode

Total characters2497095
Distinct characters29
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowSALES UNIT MEDIUM BUSINESS
2nd rowOFFICE PRODUCTS SALES AREA
3rd rowSALES UNIT MEDIUM BUSINESS
4th rowOFFICE PRODUCTS SALES AREA
5th rowSALES UNIT MEDIUM BUSINESS

Common Values

ValueCountFrequency (%)
OFFICE PRODUCTS SALES AREA 37780
35.0%
SALES UNIT MEDIUM BUSINESS 32704
30.3%
CORPORATE NORD 8644
 
8.0%
CONTACT CENTER 8392
 
7.8%
CORPORATE CENTRO SUD 6959
 
6.4%
TELEFONIA (MOBILE PHONE CHANNE 3320
 
3.1%
FURNITURE (FURNITURE CHANNEL D 2018
 
1.9%
ASSOTEAM NORD 1782
 
1.6%
VENDORS PARTNERS DIPENDENTI 1534
 
1.4%
SHOP ON LINE 1063
 
1.0%
Other values (14) 3168
 
2.9%

Length

2023-09-25T15:08:50.267901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sales 70484
18.6%
office 37780
10.0%
area 37780
10.0%
products 37780
10.0%
unit 32704
8.6%
medium 32704
8.6%
business 32704
8.6%
corporate 15603
 
4.1%
nord 10426
 
2.8%
contact 8392
 
2.2%
Other values (42) 61956
16.4%

Most occurring characters

ValueCountFrequency (%)
S 296705
11.9%
E 286108
11.5%
270951
10.9%
A 187192
 
7.5%
I 153529
 
6.1%
U 153069
 
6.1%
O 151436
 
6.1%
R 146877
 
5.9%
T 132879
 
5.3%
N 131496
 
5.3%
Other values (19) 586853
23.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2219063
88.9%
Space Separator 270951
 
10.9%
Open Punctuation 5340
 
0.2%
Other Punctuation 1013
 
< 0.1%
Dash Punctuation 726
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 296705
13.4%
E 286108
12.9%
A 187192
8.4%
I 153529
 
6.9%
U 153069
 
6.9%
O 151436
 
6.8%
R 146877
 
6.6%
T 132879
 
6.0%
N 131496
 
5.9%
C 130318
 
5.9%
Other values (13) 449454
20.3%
Other Punctuation
ValueCountFrequency (%)
/ 891
88.0%
& 122
 
12.0%
Space Separator
ValueCountFrequency (%)
270951
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5340
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 726
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2219063
88.9%
Common 278032
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 296705
13.4%
E 286108
12.9%
A 187192
8.4%
I 153529
 
6.9%
U 153069
 
6.9%
O 151436
 
6.8%
R 146877
 
6.6%
T 132879
 
6.0%
N 131496
 
5.9%
C 130318
 
5.9%
Other values (13) 449454
20.3%
Common
ValueCountFrequency (%)
270951
97.5%
( 5340
 
1.9%
/ 891
 
0.3%
- 726
 
0.3%
& 122
 
< 0.1%
) 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2497095
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 296705
11.9%
E 286108
11.5%
270951
10.9%
A 187192
 
7.5%
I 153529
 
6.1%
U 153069
 
6.1%
O 151436
 
6.1%
R 146877
 
5.9%
T 132879
 
5.3%
N 131496
 
5.3%
Other values (19) 586853
23.5%

HAS_REAL_ORDER
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size844.0 KiB
1
66888 
0
41126 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters108014
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 66888
61.9%
0 41126
38.1%

Length

2023-09-25T15:08:50.329578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-25T15:08:50.394604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 66888
61.9%
0 41126
38.1%

Most occurring characters

ValueCountFrequency (%)
1 66888
61.9%
0 41126
38.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 108014
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 66888
61.9%
0 41126
38.1%

Most occurring scripts

ValueCountFrequency (%)
Common 108014
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 66888
61.9%
0 41126
38.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108014
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 66888
61.9%
0 41126
38.1%

DEALVALUE
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct68217
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4457.0509
Minimum0
Maximum3.1816154 × 108
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size844.0 KiB
2023-09-25T15:08:50.457267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.473842
Q141.980292
median199
Q3785.97222
95-th percentile5099.805
Maximum3.1816154 × 108
Range3.1816154 × 108
Interquartile range (IQR)743.99193

Descriptive statistics

Standard deviation968135.19
Coefficient of variation (CV)217.2143
Kurtosis107983.74
Mean4457.0509
Median Absolute Deviation (MAD)185.56
Skewness328.58593
Sum4.8142389 × 108
Variance9.3728575 × 1011
MonotonicityNot monotonic
2023-09-25T15:08:50.531017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199 193
 
0.2%
189 149
 
0.1%
365 74
 
0.1%
0.01 71
 
0.1%
39 69
 
0.1%
4984.14 64
 
0.1%
3332.22 64
 
0.1%
6153.72 64
 
0.1%
999 55
 
0.1%
5381.05 51
 
< 0.1%
Other values (68207) 107160
99.2%
ValueCountFrequency (%)
0 4
 
< 0.1%
0.01 71
0.1%
0.01823709 2
 
< 0.1%
0.02 2
 
< 0.1%
0.029022 1
 
< 0.1%
0.03 9
 
< 0.1%
0.05 3
 
< 0.1%
0.09 1
 
< 0.1%
0.1 13
 
< 0.1%
0.12 1
 
< 0.1%
ValueCountFrequency (%)
318161535 1
< 0.1%
2243150 1
< 0.1%
952664 1
< 0.1%
897260 1
< 0.1%
825902 1
< 0.1%
816488.75 1
< 0.1%
730764.72 1
< 0.1%
429086 1
< 0.1%
411757.5 1
< 0.1%
379533 1
< 0.1%

DEALQTY
Real number (ℝ)

SKEWED 

Distinct412
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.351371
Minimum0
Maximum180000
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size844.0 KiB
2023-09-25T15:08:50.607443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q35
95-th percentile40
Maximum180000
Range180000
Interquartile range (IQR)4

Descriptive statistics

Standard deviation745.41137
Coefficient of variation (CV)29.403197
Kurtosis32504.611
Mean25.351371
Median Absolute Deviation (MAD)1
Skewness149.66662
Sum2738303
Variance555638.11
MonotonicityNot monotonic
2023-09-25T15:08:50.679685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 51988
48.1%
2 15781
 
14.6%
3 6723
 
6.2%
5 5130
 
4.7%
4 4704
 
4.4%
10 4548
 
4.2%
6 2208
 
2.0%
20 1927
 
1.8%
8 1170
 
1.1%
30 1149
 
1.1%
Other values (402) 12686
 
11.7%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 51988
48.1%
2 15781
 
14.6%
3 6723
 
6.2%
4 4704
 
4.4%
5 5130
 
4.7%
6 2208
 
2.0%
7 847
 
0.8%
8 1170
 
1.1%
9 542
 
0.5%
ValueCountFrequency (%)
180000 1
 
< 0.1%
57600 2
 
< 0.1%
36000 3
 
< 0.1%
28800 3
 
< 0.1%
24000 1
 
< 0.1%
22800 16
< 0.1%
21600 1
 
< 0.1%
19200 4
 
< 0.1%
18000 1
 
< 0.1%
15000 1
 
< 0.1%

ORDER_PRICE
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct53681
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean355.07135
Minimum0.009674
Maximum424215.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size844.0 KiB
2023-09-25T15:08:50.758654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.009674
5-th percentile2.44
Q115.04
median84.222825
Q3346
95-th percentile1399
Maximum424215.38
Range424215.37
Interquartile range (IQR)330.96

Descriptive statistics

Standard deviation2122.6674
Coefficient of variation (CV)5.9781433
Kurtosis18961.034
Mean355.07135
Median Absolute Deviation (MAD)78.108042
Skewness116.38911
Sum38352677
Variance4505717
MonotonicityNot monotonic
2023-09-25T15:08:50.830109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199 236
 
0.2%
189 163
 
0.2%
1538.43 124
 
0.1%
1076.21 110
 
0.1%
0.01 110
 
0.1%
699 94
 
0.1%
459 93
 
0.1%
499 92
 
0.1%
1661.38 91
 
0.1%
1477.59 89
 
0.1%
Other values (53671) 106812
98.9%
ValueCountFrequency (%)
0.009674 1
 
< 0.1%
0.01 110
0.1%
0.01823709 2
 
< 0.1%
0.02 1
 
< 0.1%
0.03 7
 
< 0.1%
0.09 1
 
< 0.1%
0.1 3
 
< 0.1%
0.10549 2
 
< 0.1%
0.105754 1
 
< 0.1%
0.10582 3
 
< 0.1%
ValueCountFrequency (%)
424215.38 1
< 0.1%
247000 1
< 0.1%
221000 1
< 0.1%
215946 1
< 0.1%
175181.5 1
< 0.1%
71961.27 2
< 0.1%
67043.4 1
< 0.1%
60669 1
< 0.1%
59619.73 1
< 0.1%
54313.2 1
< 0.1%

ORIGINAL_PRICE
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct19216
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean358.46243
Minimum0.01
Maximum424215.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size844.0 KiB
2023-09-25T15:08:50.903198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile2.49
Q115.525
median85
Q3351.185
95-th percentile1401
Maximum424215.38
Range424215.37
Interquartile range (IQR)335.66

Descriptive statistics

Standard deviation2123.9905
Coefficient of variation (CV)5.9252807
Kurtosis18913.092
Mean358.46243
Median Absolute Deviation (MAD)78.79
Skewness116.1698
Sum38718961
Variance4511335.9
MonotonicityNot monotonic
2023-09-25T15:08:50.973689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199 287
 
0.3%
189 232
 
0.2%
6.21 214
 
0.2%
699 208
 
0.2%
459 201
 
0.2%
499 196
 
0.2%
7.32 181
 
0.2%
11.86 180
 
0.2%
799 179
 
0.2%
899 168
 
0.2%
Other values (19206) 105968
98.1%
ValueCountFrequency (%)
0.01 111
0.1%
0.02 1
 
< 0.1%
0.03 9
 
< 0.1%
0.1 4
 
< 0.1%
0.11 16
 
< 0.1%
0.12 2
 
< 0.1%
0.16 2
 
< 0.1%
0.17 7
 
< 0.1%
0.18 2
 
< 0.1%
0.19 1
 
< 0.1%
ValueCountFrequency (%)
424215.38 1
< 0.1%
247000 1
< 0.1%
221000 1
< 0.1%
215946 1
< 0.1%
175181.5 1
< 0.1%
71961.27 2
< 0.1%
67043.4 1
< 0.1%
60669 1
< 0.1%
59619.73 1
< 0.1%
54313.2 1
< 0.1%

ORIGINAL_PRICE_NO_DISCOUNT_PERS
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct26617
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean361.17283
Minimum0.01
Maximum445426.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size844.0 KiB
2023-09-25T15:08:51.048012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile2.6565
Q115.939
median85.965
Q3352.9074
95-th percentile1419.831
Maximum445426.15
Range445426.14
Interquartile range (IQR)336.9684

Descriptive statistics

Standard deviation2212.0704
Coefficient of variation (CV)6.1246867
Kurtosis19763.657
Mean361.17283
Median Absolute Deviation (MAD)79.3826
Skewness119.93783
Sum39011722
Variance4893255.5
MonotonicityNot monotonic
2023-09-25T15:08:51.118964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199 265
 
0.2%
189 210
 
0.2%
699 206
 
0.2%
459 201
 
0.2%
499 196
 
0.2%
799 179
 
0.2%
11.86 178
 
0.2%
899 168
 
0.2%
999 166
 
0.2%
469 157
 
0.1%
Other values (26607) 106088
98.2%
ValueCountFrequency (%)
0.01 111
0.1%
0.02 1
 
< 0.1%
0.03 6
 
< 0.1%
0.0312 1
 
< 0.1%
0.037257 2
 
< 0.1%
0.1 4
 
< 0.1%
0.11 11
 
< 0.1%
0.12 2
 
< 0.1%
0.1232 5
 
< 0.1%
0.16 1
 
< 0.1%
ValueCountFrequency (%)
445426.149 1
< 0.1%
259350 1
< 0.1%
254150 1
< 0.1%
215946 1
< 0.1%
175181.5 1
< 0.1%
71961.27 2
< 0.1%
67043.4 1
< 0.1%
60669 1
< 0.1%
59619.73 1
< 0.1%
54313.2 1
< 0.1%
Distinct181
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size844.0 KiB
Minimum2023-03-02 00:00:00
Maximum2023-08-29 00:00:00
2023-09-25T15:08:51.190910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:51.270789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

CUSTOMER_DISCOUNT_PERS
Real number (ℝ)

ZEROS 

Distinct189
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0418276
Minimum-20
Maximum64.1
Zeros84031
Zeros (%)77.8%
Negative12
Negative (%)< 0.1%
Memory size844.0 KiB
2023-09-25T15:08:51.355788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-20
5-th percentile0
Q10
median0
Q30
95-th percentile15
Maximum64.1
Range84.1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.0167792
Coefficient of variation (CV)2.4570043
Kurtosis7.0380199
Mean2.0418276
Median Absolute Deviation (MAD)0
Skewness2.6913991
Sum220545.97
Variance25.168074
MonotonicityNot monotonic
2023-09-25T15:08:51.425103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 84031
77.8%
15 2802
 
2.6%
20 1978
 
1.8%
1 1341
 
1.2%
2 1179
 
1.1%
8 1168
 
1.1%
10 1080
 
1.0%
18 1059
 
1.0%
3 987
 
0.9%
4 939
 
0.9%
Other values (179) 11450
 
10.6%
ValueCountFrequency (%)
-20 1
 
< 0.1%
-18 1
 
< 0.1%
-10 2
 
< 0.1%
-2.5 3
 
< 0.1%
-2 1
 
< 0.1%
-1.5 2
 
< 0.1%
-1 2
 
< 0.1%
0 84031
77.8%
0.01 1
 
< 0.1%
0.03 1
 
< 0.1%
ValueCountFrequency (%)
64.1 5
 
< 0.1%
46.31 2
 
< 0.1%
38.9 1
 
< 0.1%
32 10
 
< 0.1%
30.65 6
 
< 0.1%
30 61
0.1%
28.72 2
 
< 0.1%
27.42 6
 
< 0.1%
26.25 2
 
< 0.1%
26 6
 
< 0.1%

DISCOUNT_PCT
Real number (ℝ)

ZEROS 

Distinct6022
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6882688
Minimum0
Maximum40
Zeros48159
Zeros (%)44.6%
Negative0
Negative (%)0.0%
Memory size844.0 KiB
2023-09-25T15:08:51.497598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.88
Q32
95-th percentile6.0606
Maximum40
Range40
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.5294701
Coefficient of variation (CV)2.0905854
Kurtosis36.722569
Mean1.6882688
Median Absolute Deviation (MAD)0.88
Skewness5.304876
Sum182356.67
Variance12.457159
MonotonicityNot monotonic
2023-09-25T15:08:51.581784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48159
44.6%
2 8589
 
8.0%
1 6660
 
6.2%
3 2986
 
2.8%
0.5 1753
 
1.6%
1.5 1653
 
1.5%
2.5 994
 
0.9%
4 869
 
0.8%
5 856
 
0.8%
0.2 534
 
0.5%
Other values (6012) 34961
32.4%
ValueCountFrequency (%)
0 48159
44.6%
0.002 1
 
< 0.1%
0.0039 1
 
< 0.1%
0.0093 1
 
< 0.1%
0.0095 1
 
< 0.1%
0.0115 1
 
< 0.1%
0.0133 1
 
< 0.1%
0.015 2
 
< 0.1%
0.0179 2
 
< 0.1%
0.018 1
 
< 0.1%
ValueCountFrequency (%)
40 10
< 0.1%
39.7906 1
 
< 0.1%
39.6221 1
 
< 0.1%
39.5 1
 
< 0.1%
39.2097 2
 
< 0.1%
39.2062 1
 
< 0.1%
39.0031 2
 
< 0.1%
38.9978 1
 
< 0.1%
38.989 3
 
< 0.1%
38.9842 1
 
< 0.1%
Distinct2921
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size844.0 KiB
Minimum2000-01-01 00:00:00
Maximum2023-08-28 00:00:00
2023-09-25T15:08:51.666216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:51.740531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ARTID
Text

Distinct27419
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Memory size844.0 KiB
2023-09-25T15:08:51.878238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length15
Median length12
Mean length9.0203029
Min length2

Characters and Unicode

Total characters974319
Distinct characters42
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11470 ?
Unique (%)10.6%

Sample

1st rowFI-65F
2nd rowKCN-00032
3rd rowCE313A
4th rowNX090202106
5th row9155101C
ValueCountFrequency (%)
t5d-03485 383
 
0.4%
mpww3ql/a 171
 
0.2%
n16h6 151
 
0.1%
mfcl2710dw 145
 
0.1%
634-bykr 140
 
0.1%
dell-wd19s130w 106
 
0.1%
9424697 106
 
0.1%
865414-b21 106
 
0.1%
ac140401 105
 
0.1%
p46171-a21 105
 
0.1%
Other values (27407) 106496
98.6%
2023-09-25T15:08:52.104735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 125986
 
12.9%
1 79593
 
8.2%
2 65577
 
6.7%
3 50016
 
5.1%
4 43995
 
4.5%
5 41613
 
4.3%
A 36755
 
3.8%
- 36726
 
3.8%
6 36188
 
3.7%
7 30859
 
3.2%
Other values (32) 427011
43.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 532163
54.6%
Uppercase Letter 395568
40.6%
Dash Punctuation 36726
 
3.8%
Other Punctuation 8237
 
0.8%
Connector Punctuation 1613
 
0.2%
Open Punctuation 6
 
< 0.1%
Close Punctuation 6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 36755
 
9.3%
E 29796
 
7.5%
C 24609
 
6.2%
S 24225
 
6.1%
T 23328
 
5.9%
B 23205
 
5.9%
P 19958
 
5.0%
L 19002
 
4.8%
D 17854
 
4.5%
M 17305
 
4.4%
Other values (16) 159531
40.3%
Decimal Number
ValueCountFrequency (%)
0 125986
23.7%
1 79593
15.0%
2 65577
12.3%
3 50016
 
9.4%
4 43995
 
8.3%
5 41613
 
7.8%
6 36188
 
6.8%
7 30859
 
5.8%
8 29412
 
5.5%
9 28924
 
5.4%
Other Punctuation
ValueCountFrequency (%)
/ 4810
58.4%
. 3427
41.6%
Dash Punctuation
ValueCountFrequency (%)
- 36726
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1613
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 578751
59.4%
Latin 395568
40.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 36755
 
9.3%
E 29796
 
7.5%
C 24609
 
6.2%
S 24225
 
6.1%
T 23328
 
5.9%
B 23205
 
5.9%
P 19958
 
5.0%
L 19002
 
4.8%
D 17854
 
4.5%
M 17305
 
4.4%
Other values (16) 159531
40.3%
Common
ValueCountFrequency (%)
0 125986
21.8%
1 79593
13.8%
2 65577
11.3%
3 50016
 
8.6%
4 43995
 
7.6%
5 41613
 
7.2%
- 36726
 
6.3%
6 36188
 
6.3%
7 30859
 
5.3%
8 29412
 
5.1%
Other values (6) 38786
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 974319
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 125986
 
12.9%
1 79593
 
8.2%
2 65577
 
6.7%
3 50016
 
5.1%
4 43995
 
4.5%
5 41613
 
4.3%
A 36755
 
3.8%
- 36726
 
3.8%
6 36188
 
3.7%
7 30859
 
3.2%
Other values (32) 427011
43.8%

ARTDSC
Text

Distinct26068
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Memory size844.0 KiB
2023-09-25T15:08:52.331963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length36
Median length30
Mean length27.219916
Min length3

Characters and Unicode

Total characters2940132
Distinct characters80
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10742 ?
Unique (%)9.9%

Sample

1st rowFI-65F
2nd rowSRFC GO TYPE COVER BLACK
3rd row126A MAGENTA LASERJET PRINT CARTR.
4th rowCAVO DP/DP LSOH M/M MT10
5th rowHELIOS IP VERSO - UNIT BASE CON CA
ValueCountFrequency (%)
hp 6055
 
1.2%
toner 5003
 
1.0%
4760
 
0.9%
nero 4500
 
0.9%
black 4127
 
0.8%
pro 3508
 
0.7%
usb 2775
 
0.5%
monitor 2755
 
0.5%
g9 2681
 
0.5%
dell 2574
 
0.5%
Other values (22701) 470037
92.4%
2023-09-25T15:08:52.634827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
411587
 
14.0%
A 162467
 
5.5%
E 159987
 
5.4%
T 136490
 
4.6%
O 136156
 
4.6%
I 135016
 
4.6%
R 133301
 
4.5%
S 118224
 
4.0%
C 111914
 
3.8%
L 102656
 
3.5%
Other values (70) 1332334
45.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1952050
66.4%
Decimal Number 482799
 
16.4%
Space Separator 411587
 
14.0%
Other Punctuation 41972
 
1.4%
Dash Punctuation 30727
 
1.0%
Math Symbol 9554
 
0.3%
Lowercase Letter 4826
 
0.2%
Open Punctuation 2030
 
0.1%
Close Punctuation 1795
 
0.1%
Connector Punctuation 1492
 
0.1%
Other values (3) 1300
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 162467
 
8.3%
E 159987
 
8.2%
T 136490
 
7.0%
O 136156
 
7.0%
I 135016
 
6.9%
R 133301
 
6.8%
S 118224
 
6.1%
C 111914
 
5.7%
L 102656
 
5.3%
N 100912
 
5.2%
Other values (18) 654927
33.6%
Other Punctuation
ValueCountFrequency (%)
/ 22234
53.0%
. 17503
41.7%
: 756
 
1.8%
, 714
 
1.7%
" 238
 
0.6%
! 230
 
0.5%
' 200
 
0.5%
* 44
 
0.1%
& 41
 
0.1%
? 10
 
< 0.1%
Other values (2) 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 95953
19.9%
1 82266
17.0%
2 77927
16.1%
5 54176
11.2%
3 43866
9.1%
4 38805
8.0%
6 30843
 
6.4%
8 23184
 
4.8%
7 20686
 
4.3%
9 15093
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
i 1270
26.3%
t 1016
21.1%
o 762
15.8%
z 508
 
10.5%
r 254
 
5.3%
u 254
 
5.3%
a 254
 
5.3%
n 254
 
5.3%
f 254
 
5.3%
Control
ValueCountFrequency (%)
€ 237
78.2%
™ 33
 
10.9%
‰ 19
 
6.3%
ˆ 6
 
2.0%
œ 5
 
1.7%
– 3
 
1.0%
Math Symbol
ValueCountFrequency (%)
+ 4780
50.0%
> 3176
33.2%
= 1591
 
16.7%
< 7
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 2027
99.9%
[ 3
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 1792
99.8%
] 3
 
0.2%
Currency Symbol
ValueCountFrequency (%)
£ 757
95.9%
$ 32
 
4.1%
Other Symbol
ValueCountFrequency (%)
° 202
97.1%
® 6
 
2.9%
Space Separator
ValueCountFrequency (%)
411587
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30727
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1492
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1956876
66.6%
Common 983256
33.4%

Most frequent character per script

Common
ValueCountFrequency (%)
411587
41.9%
0 95953
 
9.8%
1 82266
 
8.4%
2 77927
 
7.9%
5 54176
 
5.5%
3 43866
 
4.5%
4 38805
 
3.9%
6 30843
 
3.1%
- 30727
 
3.1%
8 23184
 
2.4%
Other values (33) 93922
 
9.6%
Latin
ValueCountFrequency (%)
A 162467
 
8.3%
E 159987
 
8.2%
T 136490
 
7.0%
O 136156
 
7.0%
I 135016
 
6.9%
R 133301
 
6.8%
S 118224
 
6.0%
C 111914
 
5.7%
L 102656
 
5.2%
N 100912
 
5.2%
Other values (27) 659753
33.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2937596
99.9%
None 2536
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
411587
 
14.0%
A 162467
 
5.5%
E 159987
 
5.4%
T 136490
 
4.6%
O 136156
 
4.6%
I 135016
 
4.6%
R 133301
 
4.5%
S 118224
 
4.0%
C 111914
 
3.8%
L 102656
 
3.5%
Other values (59) 1329798
45.3%
None
ValueCountFrequency (%)
 965
38.1%
£ 757
29.9%
à 303
 
11.9%
€ 237
 
9.3%
° 202
 
8.0%
™ 33
 
1.3%
‰ 19
 
0.7%
ˆ 6
 
0.2%
® 6
 
0.2%
œ 5
 
0.2%
Distinct676
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size844.0 KiB
2023-09-25T15:08:52.850025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.958922
Min length2

Characters and Unicode

Total characters319605
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)< 0.1%

Sample

1st rowFUJ
2nd rowSUA
3rd rowYHP
4th rowNEO
5th row2NN
ValueCountFrequency (%)
de1 6039
 
5.6%
hp 4437
 
4.1%
yhp 4085
 
3.8%
yep 2124
 
2.0%
dee 2094
 
1.9%
neo 1857
 
1.7%
wpe 1780
 
1.6%
len 1777
 
1.6%
hpe 1751
 
1.6%
cel 1212
 
1.1%
Other values (666) 80858
74.9%
2023-09-25T15:08:53.291217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 31749
 
9.9%
P 28877
 
9.0%
H 21409
 
6.7%
S 20786
 
6.5%
A 19117
 
6.0%
5 18049
 
5.6%
Y 17513
 
5.5%
N 15691
 
4.9%
L 14812
 
4.6%
D 13839
 
4.3%
Other values (24) 117763
36.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 288621
90.3%
Decimal Number 30984
 
9.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 31749
 
11.0%
P 28877
 
10.0%
H 21409
 
7.4%
S 20786
 
7.2%
A 19117
 
6.6%
Y 17513
 
6.1%
N 15691
 
5.4%
L 14812
 
5.1%
D 13839
 
4.8%
C 12681
 
4.4%
Other values (16) 92147
31.9%
Decimal Number
ValueCountFrequency (%)
5 18049
58.3%
1 7912
25.5%
2 4094
 
13.2%
9 378
 
1.2%
7 218
 
0.7%
3 180
 
0.6%
8 148
 
0.5%
0 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 288621
90.3%
Common 30984
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 31749
 
11.0%
P 28877
 
10.0%
H 21409
 
7.4%
S 20786
 
7.2%
A 19117
 
6.6%
Y 17513
 
6.1%
N 15691
 
5.4%
L 14812
 
5.1%
D 13839
 
4.8%
C 12681
 
4.4%
Other values (16) 92147
31.9%
Common
ValueCountFrequency (%)
5 18049
58.3%
1 7912
25.5%
2 4094
 
13.2%
9 378
 
1.2%
7 218
 
0.7%
3 180
 
0.6%
8 148
 
0.5%
0 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319605
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 31749
 
9.9%
P 28877
 
9.0%
H 21409
 
6.7%
S 20786
 
6.5%
A 19117
 
6.0%
5 18049
 
5.6%
Y 17513
 
5.5%
N 15691
 
4.9%
L 14812
 
4.6%
D 13839
 
4.3%
Other values (24) 117763
36.8%
Distinct378
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size844.0 KiB
2023-09-25T15:08:53.491517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.0095913
Min length2

Characters and Unicode

Total characters217064
Distinct characters45
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)< 0.1%

Sample

1st rowSH
2nd rowAM
3rd rowMC
4th rowEG
5th rowWV
ValueCountFrequency (%)
mc 12889
 
11.9%
no 5815
 
5.4%
mo 5170
 
4.8%
1e 3958
 
3.7%
vf 3042
 
2.8%
1n 3003
 
2.8%
pc 2872
 
2.7%
es 2821
 
2.6%
tm 2500
 
2.3%
qf 2150
 
2.0%
Other values (368) 63794
59.1%
2023-09-25T15:08:53.760794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 29054
13.4%
C 22695
 
10.5%
N 17802
 
8.2%
1 17264
 
8.0%
O 13817
 
6.4%
E 12212
 
5.6%
T 8860
 
4.1%
H 8647
 
4.0%
P 7667
 
3.5%
S 7056
 
3.3%
Other values (35) 71990
33.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 186383
85.9%
Decimal Number 29117
 
13.4%
Lowercase Letter 1016
 
0.5%
Close Punctuation 254
 
0.1%
Open Punctuation 254
 
0.1%
Currency Symbol 32
 
< 0.1%
Connector Punctuation 8
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 29054
15.6%
C 22695
12.2%
N 17802
 
9.6%
O 13817
 
7.4%
E 12212
 
6.6%
T 8860
 
4.8%
H 8647
 
4.6%
P 7667
 
4.1%
S 7056
 
3.8%
F 6889
 
3.7%
Other values (17) 51684
27.7%
Decimal Number
ValueCountFrequency (%)
1 17264
59.3%
0 4701
 
16.1%
3 2626
 
9.0%
2 1088
 
3.7%
4 1079
 
3.7%
6 989
 
3.4%
9 670
 
2.3%
5 323
 
1.1%
7 216
 
0.7%
8 161
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
l 508
50.0%
u 254
25.0%
n 254
25.0%
Currency Symbol
ValueCountFrequency (%)
£ 20
62.5%
$ 12
37.5%
Close Punctuation
ValueCountFrequency (%)
) 254
100.0%
Open Punctuation
ValueCountFrequency (%)
( 254
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 187399
86.3%
Common 29665
 
13.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 29054
15.5%
C 22695
12.1%
N 17802
 
9.5%
O 13817
 
7.4%
E 12212
 
6.5%
T 8860
 
4.7%
H 8647
 
4.6%
P 7667
 
4.1%
S 7056
 
3.8%
F 6889
 
3.7%
Other values (20) 52700
28.1%
Common
ValueCountFrequency (%)
1 17264
58.2%
0 4701
 
15.8%
3 2626
 
8.9%
2 1088
 
3.7%
4 1079
 
3.6%
6 989
 
3.3%
9 670
 
2.3%
5 323
 
1.1%
) 254
 
0.9%
( 254
 
0.9%
Other values (5) 417
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 217024
> 99.9%
None 40
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 29054
13.4%
C 22695
 
10.5%
N 17802
 
8.2%
1 17264
 
8.0%
O 13817
 
6.4%
E 12212
 
5.6%
T 8860
 
4.1%
H 8647
 
4.0%
P 7667
 
3.5%
S 7056
 
3.3%
Other values (33) 71950
33.2%
None
ValueCountFrequency (%)
 20
50.0%
£ 20
50.0%
Distinct383
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size844.0 KiB
2023-09-25T15:08:53.907004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length53
Median length37
Mean length16.403327
Min length3

Characters and Unicode

Total characters1771789
Distinct characters42
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)< 0.1%

Sample

1st rowSCANNER
2nd rowACCESSORI TABLET E EBOOK READER
3rd rowMATERIALE DI CONSUMO
4th rowCAVI AUDIO-VIDEO
5th rowVIDEOCITOFONI IP
ValueCountFrequency (%)
e 24320
 
9.6%
di 15121
 
6.0%
materiale 12889
 
5.1%
consumo 12889
 
5.1%
desktop 8042
 
3.2%
notebook 6893
 
2.7%
accessori 6568
 
2.6%
monitor 5988
 
2.4%
software 5184
 
2.0%
4589
 
1.8%
Other values (493) 150573
59.5%
2023-09-25T15:08:54.134170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 193378
10.9%
I 177655
10.0%
O 163940
 
9.3%
145042
 
8.2%
T 139088
 
7.9%
A 133569
 
7.5%
R 120116
 
6.8%
S 97967
 
5.5%
C 93237
 
5.3%
N 85056
 
4.8%
Other values (32) 422741
23.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1608624
90.8%
Space Separator 145042
 
8.2%
Dash Punctuation 6643
 
0.4%
Other Punctuation 3355
 
0.2%
Close Punctuation 3305
 
0.2%
Open Punctuation 3305
 
0.2%
Lowercase Letter 1016
 
0.1%
Control 438
 
< 0.1%
Decimal Number 61
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 193378
12.0%
I 177655
11.0%
O 163940
10.2%
T 139088
8.6%
A 133569
 
8.3%
R 120116
 
7.5%
S 97967
 
6.1%
C 93237
 
5.8%
N 85056
 
5.3%
L 63302
 
3.9%
Other values (17) 341316
21.2%
Other Punctuation
ValueCountFrequency (%)
/ 2930
87.3%
. 271
 
8.1%
' 60
 
1.8%
& 58
 
1.7%
, 36
 
1.1%
Lowercase Letter
ValueCountFrequency (%)
l 508
50.0%
u 254
25.0%
n 254
25.0%
Control
ValueCountFrequency (%)
€ 426
97.3%
ˆ 12
 
2.7%
Space Separator
ValueCountFrequency (%)
145042
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6643
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3305
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3305
100.0%
Decimal Number
ValueCountFrequency (%)
3 61
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1609640
90.8%
Common 162149
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 193378
12.0%
I 177655
11.0%
O 163940
10.2%
T 139088
8.6%
A 133569
 
8.3%
R 120116
 
7.5%
S 97967
 
6.1%
C 93237
 
5.8%
N 85056
 
5.3%
L 63302
 
3.9%
Other values (20) 342332
21.3%
Common
ValueCountFrequency (%)
145042
89.4%
- 6643
 
4.1%
) 3305
 
2.0%
( 3305
 
2.0%
/ 2930
 
1.8%
€ 426
 
0.3%
. 271
 
0.2%
3 61
 
< 0.1%
' 60
 
< 0.1%
& 58
 
< 0.1%
Other values (2) 48
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1770913
> 99.9%
None 876
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 193378
10.9%
I 177655
10.0%
O 163940
 
9.3%
145042
 
8.2%
T 139088
 
7.9%
A 133569
 
7.5%
R 120116
 
6.8%
S 97967
 
5.5%
C 93237
 
5.3%
N 85056
 
4.8%
Other values (29) 421865
23.8%
None
ValueCountFrequency (%)
à 438
50.0%
€ 426
48.6%
ˆ 12
 
1.4%
Distinct675
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size844.0 KiB
2023-09-25T15:08:54.317136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length35
Median length29
Mean length10.954247
Min length2

Characters and Unicode

Total characters1183212
Distinct characters40
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)< 0.1%

Sample

1st rowFUJITSU
2nd rowMICROSOFT SURFACE COMMERCIAL
3rd rowCONSUMABILI HP
4th rowNILOX PC COMPONENTS
5th row2N
ValueCountFrequency (%)
consumabili 10953
 
6.2%
hp 10358
 
5.9%
dell 8134
 
4.6%
inc 4437
 
2.5%
server 4326
 
2.4%
networking 3418
 
1.9%
hpe 3237
 
1.8%
epson 3114
 
1.8%
samsung 2935
 
1.7%
lenovo 2763
 
1.6%
Other values (755) 123105
69.6%
2023-09-25T15:08:54.584930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 117588
 
9.9%
O 103077
 
8.7%
I 92594
 
7.8%
N 85503
 
7.2%
S 72484
 
6.1%
A 72161
 
6.1%
L 72089
 
6.1%
R 70283
 
5.9%
68766
 
5.8%
T 67119
 
5.7%
Other values (30) 361548
30.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1105738
93.5%
Space Separator 68766
 
5.8%
Other Punctuation 4019
 
0.3%
Dash Punctuation 3167
 
0.3%
Decimal Number 1522
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 117588
10.6%
O 103077
 
9.3%
I 92594
 
8.4%
N 85503
 
7.7%
S 72484
 
6.6%
A 72161
 
6.5%
L 72089
 
6.5%
R 70283
 
6.4%
T 67119
 
6.1%
C 61726
 
5.6%
Other values (16) 291114
26.3%
Decimal Number
ValueCountFrequency (%)
2 1226
80.6%
3 181
 
11.9%
1 34
 
2.2%
9 34
 
2.2%
8 34
 
2.2%
0 6
 
0.4%
7 5
 
0.3%
4 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 3207
79.8%
& 671
 
16.7%
/ 128
 
3.2%
' 13
 
0.3%
Space Separator
ValueCountFrequency (%)
68766
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3167
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1105738
93.5%
Common 77474
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 117588
10.6%
O 103077
 
9.3%
I 92594
 
8.4%
N 85503
 
7.7%
S 72484
 
6.6%
A 72161
 
6.5%
L 72089
 
6.5%
R 70283
 
6.4%
T 67119
 
6.1%
C 61726
 
5.6%
Other values (16) 291114
26.3%
Common
ValueCountFrequency (%)
68766
88.8%
. 3207
 
4.1%
- 3167
 
4.1%
2 1226
 
1.6%
& 671
 
0.9%
3 181
 
0.2%
/ 128
 
0.2%
1 34
 
< 0.1%
9 34
 
< 0.1%
8 34
 
< 0.1%
Other values (4) 26
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1183212
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 117588
 
9.9%
O 103077
 
8.7%
I 92594
 
7.8%
N 85503
 
7.2%
S 72484
 
6.1%
A 72161
 
6.1%
L 72089
 
6.1%
R 70283
 
5.9%
68766
 
5.8%
T 67119
 
5.7%
Other values (30) 361548
30.6%
Distinct1839
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size844.0 KiB
Minimum2001-01-01 00:00:00
Maximum2023-07-31 00:00:00
2023-09-25T15:08:54.687319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:54.763609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-09-25T15:08:47.304915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:40.683800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:41.498367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:42.302885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:43.017331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:43.730461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:44.446821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:45.234036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:45.926514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:46.614464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:47.383084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:40.812933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:41.568887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:42.374300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:43.088395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:43.799555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:44.515481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:45.305376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:45.996564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:46.688841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:47.455382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:40.923852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:41.638136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:42.448064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:43.159861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:43.871079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:44.584462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:45.374707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:46.066559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:46.758315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:47.529101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:41.005072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:41.709699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:42.518930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:43.228925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:43.942595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:44.653381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:45.443695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:46.137313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:46.826306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:47.606752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:41.074968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:41.783029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:42.591362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:43.300154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:44.015979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:44.723593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:45.514910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:46.207281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:46.896227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:47.684026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:41.146024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:41.855987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:42.663430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:43.374089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:44.088775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:44.902030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:45.587110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:46.280166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:46.965560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:47.755730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:41.211273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:41.923155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:42.730161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:43.443447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:44.156554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:44.964152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:45.652948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:46.344893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:47.033701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:47.825257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:41.277755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:41.989491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:42.797617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:43.511593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:44.223595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:45.026792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:45.717029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:46.409528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:47.102213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:47.898736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:41.351331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:42.151076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:42.870133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:43.583405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:44.295215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:45.091124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:45.783728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:46.473595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:47.168919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:47.968273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:41.422124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:42.220183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:42.941190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:43.654077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:44.369893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:45.157391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:45.850731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:46.540905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-25T15:08:47.230959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-25T15:08:54.835589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Unnamed: 0DEALKEYDEALDETKEYDEALVALUEDEALQTYORDER_PRICEORIGINAL_PRICEORIGINAL_PRICE_NO_DISCOUNT_PERSCUSTOMER_DISCOUNT_PERSDISCOUNT_PCTCHANNELIDCHANNELDSCHAS_REAL_ORDER
Unnamed: 01.0000.0020.002-0.0040.001-0.005-0.005-0.005-0.0010.0050.0040.0040.000
DEALKEY0.0021.0001.000-0.0500.023-0.059-0.059-0.062-0.1500.0350.0570.0490.045
DEALDETKEY0.0021.0001.000-0.0510.022-0.060-0.060-0.062-0.1500.0350.0570.0490.049
DEALVALUE-0.004-0.050-0.0511.0000.2790.8090.8080.808-0.253-0.2970.0000.0000.000
DEALQTY0.0010.0230.0220.2791.000-0.270-0.270-0.2710.040-0.0660.0230.0230.021
ORDER_PRICE-0.005-0.059-0.0600.809-0.2701.0001.0001.000-0.273-0.2510.0000.0000.008
ORIGINAL_PRICE-0.005-0.059-0.0600.808-0.2701.0001.0001.000-0.275-0.2410.0000.0000.008
ORIGINAL_PRICE_NO_DISCOUNT_PERS-0.005-0.062-0.0620.808-0.2711.0001.0001.000-0.260-0.2410.0000.0000.008
CUSTOMER_DISCOUNT_PERS-0.001-0.150-0.150-0.2530.040-0.273-0.275-0.2601.0000.0210.1440.1440.139
DISCOUNT_PCT0.0050.0350.035-0.297-0.066-0.251-0.241-0.2410.0211.0000.1010.1020.207
CHANNELID0.0040.0570.0570.0000.0230.0000.0000.0000.1440.1011.0001.0000.199
CHANNELDSC0.0040.0490.0490.0000.0230.0000.0000.0000.1440.1021.0001.0000.200
HAS_REAL_ORDER0.0000.0450.0490.0000.0210.0080.0080.0080.1390.2070.1990.2001.000

Missing values

2023-09-25T15:08:48.216373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-25T15:08:48.783405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0DEALKEYDEALDETKEYCUSTOMERIDCHANNELIDCHANNELDSCHAS_REAL_ORDERDEALVALUEDEALQTYORDER_PRICEORIGINAL_PRICEORIGINAL_PRICE_NO_DISCOUNT_PERSQUOTATION_DATECUSTOMER_DISCOUNT_PERSDISCOUNT_PCTARTID_DATE_CREATEARTIDARTDSCBRANDIDCATEGORYIDCATEGORYDSCBRANDDSCCUSTOMER_CREATE_DATE
003682848.012089692.0053e113f9bSMSALES UNIT MEDIUM BUSINESS1251.4778001.0251.477800256.61257.893052023-05-090.52.002013-08-28FI-65FFI-65FFUJSHSCANNERFUJITSU2011-02-01
113644306.011997831.0b6fc01a771CSOFFICE PRODUCTS SALES AREA167.2574001.067.25740068.6368.630002023-03-080.02.002020-05-13KCN-00032SRFC GO TYPE COVER BLACKSUAAMACCESSORI TABLET E EBOOK READERMICROSOFT SURFACE COMMERCIAL2006-01-01
223683424.012091190.09b2a287a25SMSALES UNIT MEDIUM BUSINESS151.8879171.051.88791752.6352.630002023-05-090.01.412010-10-25CE313A126A MAGENTA LASERJET PRINT CARTR.YHPMCMATERIALE DI CONSUMOCONSUMABILI HP2006-01-01
333656576.012027561.064354f1917CSOFFICE PRODUCTS SALES AREA120.1488001.020.14880020.5620.560002023-03-250.02.002015-12-17NX090202106CAVO DP/DP LSOH M/M MT10NEOEGCAVI AUDIO-VIDEONILOX PC COMPONENTS2006-01-01
443679663.012081975.0df63f09ef0SMSALES UNIT MEDIUM BUSINESS1726.4740001.0726.474000741.30741.300002023-05-030.02.002017-05-159155101CHELIOS IP VERSO - UNIT BASE CON CA2NNWVVIDEOCITOFONI IP2N2006-01-01
553724017.012188409.021e1cd63f1SMSALES UNIT MEDIUM BUSINESS05450.0000002.02725.0000002725.002725.000002023-07-120.00.002022-11-28M7010W18ARIN=>>CELSIUS M7010FJTPBWORKSTATIONFUJITSU TECHNOLOGY SOLUTIONS GMBH2006-01-01
663650885.012013150.07c656eaffeSMSALES UNIT MEDIUM BUSINESS114.6066001.014.60660014.6814.900202023-03-161.50.502020-12-104XD0K25030ESSENTIAL STEREO ANALOG HEADSETLENHRCUFFIE E AURICOLARILENOVO2017-02-07
773709768.012154652.0ef82ad5100SMSALES UNIT MEDIUM BUSINESS03734.0000001.03734.0000003734.003734.000002023-06-200.00.002020-12-29RS3621XSPRS3621XS+SNLNNNASSYNOLOGY2006-01-01
883688728.012103661.0438f7f62eaCSOFFICE PRODUCTS SALES AREA0290.0500005.058.01000058.0158.010002023-05-170.00.002023-03-27NXM19FHD01MONITOR 18 5 TN 5MS VGA + HDMI 75HZNHWMOMONITOR DESKTOPNILOX MONITOR2006-01-01
993725673.012192333.071d26c468aCCCONTACT CENTER09.6245921.09.6245929.919.910002023-07-160.02.882014-03-0618679TRINO HD VIDEO WEBCAMTRSWEWEB-CAMTRUST2006-01-01
Unnamed: 0DEALKEYDEALDETKEYCUSTOMERIDCHANNELIDCHANNELDSCHAS_REAL_ORDERDEALVALUEDEALQTYORDER_PRICEORIGINAL_PRICEORIGINAL_PRICE_NO_DISCOUNT_PERSQUOTATION_DATECUSTOMER_DISCOUNT_PERSDISCOUNT_PCTARTID_DATE_CREATEARTIDARTDSCBRANDIDCATEGORYIDCATEGORYDSCBRANDDSCCUSTOMER_CREATE_DATE
1080041080043738901.012225963.033d8399d6aSMSALES UNIT MEDIUM BUSINESS1369.0000001.0369.000000369.00369.00002023-08-090.00.002015-05-21JG960AHP 1950-24G-2SFP+-2XGT SWITCHHPNNSSWITCHHPE NETWORKING2006-01-01
1080051080053642774.011993881.0aa04da4a87FUFURNITURE (FURNITURE CHANNEL D1395.7645121.0395.764512399.52415.50082023-03-064.00.942020-08-04F2WV5S8S0ELAV SLIM F2WV5S8S0E 8KG C TURBSTEAMLGS03LAVATRICILG ELETTRODOMESTICI2015-03-19
1080061080063701607.012133739.0e95c2227ccCSOFFICE PRODUCTS SALES AREA1313.5958802.0156.797940159.90159.90002023-06-070.01.942022-02-1227M1N3200VA27 MOMENTUM GAMING MONITOR VAPHIMOMONITOR DESKTOPPHILIPS2006-01-01
1080071080073727030.012195872.0e3b4be0e50CCCONTACT CENTER04576.4200001.04576.4200004576.424576.42002023-07-180.00.002021-10-08WGM29002103TRADE UP M290 CON 3 ANNI TOTALWTGNFFIREWALL E UTMWATCHGUARD2018-01-29
1080081080083652172.012016467.0438f7f62eaCSOFFICE PRODUCTS SALES AREA1264.1431003.088.04770089.0089.00002023-03-200.01.072022-03-03NXM27FHD02MONITOR 27 FHD HDMI VGA BASICNHWMOMONITOR DESKTOPNILOX MONITOR2006-01-01
1080091080093708545.012151294.01972a513cfGDGDO / GDS013439.00000010.01343.9000001343.901343.90002023-06-190.00.002023-04-275F150EAZ2 MINI G9 I7 UMA 32/1TB W11PHPPBWORKSTATIONHP INC2018-04-04
1080101080103714399.012166039.01cdae8071dCSOFFICE PRODUCTS SALES AREA112.1473001.012.14730012.2712.27002023-06-270.01.002021-04-06MPWE5MBICF6 CHALK MARKER MEDIO BIANCO5OS1ESCRITTURA E CORREZIONEOSAMA2013-02-06
1080111080113658185.012031622.0eca5263750CSOFFICE PRODUCTS SALES AREA145.1835902.022.59179522.8522.85002023-03-280.01.132011-06-20PR1438RTIMBRO ROSSO 14X38MM (CONF.6PZ)5BH1ZTIMBRIBROTHER2016-06-17
1080121080123660095.012036046.0d1730100aaCSOFFICE PRODUCTS SALES AREA099.00000050.01.9800001.981.98002023-03-300.00.002019-06-12LKMOS04MINI MOUSE OTTICO USB 3 TASTINEOTMTASTIERE E MOUSENILOX PC COMPONENTS2017-11-20
1080131080133647608.012005566.0024d00a380SMSALES UNIT MEDIUM BUSINESS1712.4714002.0356.235700359.00359.00002023-03-130.00.772022-03-09E1600WKAT-BD10ME1600WKAT/15 6 /N4500/256/4/ENDASDIOPC ALL IN ONEASUS DESKTOP2006-01-01